The present invention relates generally to the field of medical imaging systems. Particularly, the present invention relates to methods for preprocessing digital mammography images in conjunction with a mammography computer-aided detection (CAD) server and a digital mammography workstation.
Digital mammogram preprocessing includes chestwall laterality detection, border artifact clipping, breast tissue segmentation, pectoral muscle segmentation, and image normalization. The results of the preprocessing are usually used by a CAD server to detect abnormalities within the breast segmented areas of normalized mammogram images. The results of the preprocessing are also used as inputs for a mammography workstation, where the bright borders present on an unprocessed image need to be clipped using the correctly identified laterality for a standard image hanging protocol. The separate segmentations of each region in the breast also improve the image contrast optimization or the intensity inversion on the mammography workstation.
The existing methods for breast segmentation are usually based on one of two methods. In the first method, the segmentation is based on a number of nearest neighbor pixels within a region that is grown from a seed point. In the second method, a gradient threshold is used to determine the inside or outside of a segmentation region. Processing using these types of algorithms is computationally slow. A region growing method or gradient method also only will detect one connected region, so the methods can not handle a mammography cleavage view, which includes the medial portions of both right and left breasts.
A typical algorithm for pectoral muscle segmentation is based on a single-line Hough transform to model the edge as a straight line between the breast tissue and the pectoral muscle. So the segmentation result cannot accurately represent the curved shape of the pectoral muscle. Alternatively, a generalized Hough transform can be used to model a curved shape; however its calculation is more expansive than the single line approach, resulting in slower processing time.